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Bhartiya Krishi Anusandhan Patrika, volume 37 issue 1 (march 2022) : 18-22

Agricultural Price Forecasting using Decomposition-based Hybrid Model

Kapil Choudhary, Girish Kumar Jha, Rajeev Ranjan Kumar, Ronit Jaiswal
1ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India.

  • Submitted18-01-2022|

  • Accepted11-05-2022|

  • First Online 16-05-2022|

  • doi 10.18805/BKAP435

Cite article:- Choudhary Kapil, Jha Kumar Girish, Kumar Ranjan Rajeev, Jaiswal Ronit (2022). Agricultural Price Forecasting using Decomposition-based Hybrid Model. Bhartiya Krishi Anusandhan Patrika. 37(1): 18-22. doi: 10.18805/BKAP435.

ABSTRACT

Agricultural price information needs for decision-making at all levels are increasing due to globalization and market integration. Due to its great reliance on biological processes, agricultural price forecasting is one of the most difficult fields of time series analysis. In this paper, a neural network model based on empirical mode decomposition is used to forecast potato prices. The monthly wholesale price series of potato from Chennai market was decomposed into five independent intrinsic modes (IMFs) and one residual with various frequencies. Then, to forecast these IMFs and residual components independently, an artificial neural network with a single hidden layer was built. Finally, the ensemble output for the original price series is formed by aggregating the forecast outcomes of all IMFs, including residuals. In terms of root mean square error and directional prediction statistics, empirical data show that the suggested ensemble model outperforms a single model.

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